ترغب بنشر مسار تعليمي؟ اضغط هنا

RF-Trojan: Leaking Kernel Data Using Register File Trojan

54   0   0.0 ( 0 )
 نشر من قبل Mohamamd Nasim Imtiaz Khan
 تاريخ النشر 2019
  مجال البحث الهندسة المعلوماتية
والبحث باللغة English




اسأل ChatGPT حول البحث

Register Files (RFs) are the most frequently accessed memories in a microprocessor for fast and efficient computation and control logic. Segment registers and control registers are especially critical for maintaining the CPU mode of execution that determinesthe access privileges. In this work, we explore the vulnerabilities in RF and propose a class of hardware Trojans which can inject faults during read or retention mode. The Trojan trigger is activated if one pre-selected address of L1 data-cache is hammered for certain number of times. The trigger evades post-silicon test since the required number of hammering to trigger is significantly high even under process and temperature variation. Once activated, the trigger can deliver payloads to cause Bitcell Corruption (BC) and inject read error by Read Port (RP) and Local Bitline (LBL). We model the Trojan in GEM5 architectural simulator performing a privilege escalation. We propose countermeasures such as read verification leveraging multiport feature, securing control and segment registers by hashing and L1 address obfuscation.



قيم البحث

اقرأ أيضاً

We present a novel proof-of-concept attack named Trojan of Things (ToT), which aims to attack NFC- enabled mobile devices such as smartphones. The key idea of ToT attacks is to covertly embed maliciously programmed NFC tags into common objects routin ely encountered in daily life such as banknotes, clothing, or furniture, which are not considered as NFC touchpoints. To fully explore the threat of ToT, we develop two striking techniques named ToT device and Phantom touch generator. These techniques enable an attacker to carry out various severe and sophisticated attacks unbeknownst to the device owner who unintentionally puts the device close to a ToT. We discuss the feasibility of the attack as well as the possible countermeasures against the threats of ToT attacks.
With the popularity of deep learning (DL), artificial intelligence (AI) has been applied in many areas of human life. Neural network or artificial neural network (NN), the main technique behind DL, has been extensively studied to facilitate computer vision and natural language recognition. However, the more we rely on information technology, the more vulnerable we are. That is, malicious NNs could bring huge threat in the so-called coming AI era. In this paper, for the first time in the literature, we propose a novel approach to design and insert powerful neural-level trojans or PoTrojan in pre-trained NN models. Most of the time, PoTrojans remain inactive, not affecting the normal functions of their host NN models. PoTrojans could only be triggered in very rare conditions. Once activated, however, the PoTrojans could cause the host NN models to malfunction, either falsely predicting or classifying, which is a significant threat to human society of the AI era. We would explain the principles of PoTrojans and the easiness of designing and inserting them in pre-trained deep learning models. PoTrojans doesnt modify the existing architecture or parameters of the pre-trained models, without re-training. Hence, the proposed method is very efficient.
Design companies often outsource their integrated circuit (IC) fabrication to third parties where ICs are susceptible to malicious acts such as the insertion of a side-channel hardware trojan horse (SCT). In this paper, we present a framework for des igning and inserting an SCT based on an engineering change order (ECO) flow, which makes it the first to disclose how effortlessly a trojan can be inserted into an IC. The trojan is designed with the goal of leaking multiple bits per power signature reading. Our findings and results show that a rogue element within a foundry has, today, all means necessary for performing a foundry-side attack via ECO.
104 - David Jewitt 2017
The Trojan asteroids of Jupiter and Neptune are likely to have been captured from original heliocentric orbits in the dynamically excited (hot) population of the Kuiper belt. However, it has long been known that the optical color distributions of the Jovian Trojans and the hot population are not alike. This difference has been reconciled with the capture hypothesis by assuming that the Trojans were resurfaced (for example, by sublimation of near-surface volatiles) upon inward migration from the Kuiper belt (where blackbody temperatures are $sim$40 K) to Jupiters orbit ($sim$125 K). Here, we examine the optical color distribution of the textit{Neptunian} Trojans using a combination of new optical photometry and published data. We find a color distribution that is statistically indistinguishable from that of the Jovian Trojans but unlike any sub-population in the Kuiper belt. This result is puzzling, because the Neptunian Trojans are very cold (blackbody temperature $sim$50 K) and a thermal process acting to modify the surface colors at Neptunes distance would also affect the Kuiper belt objects beyond, where the temperatures are nearly identical. The distinctive color distributions of the Jovian and Neptunian Trojans thus present us with a conundrum: they are very similar to each other, suggesting either capture from a common source or surface modification by a common process. However, the color distributions differ from any plausible common source population, and there is no known modifying process that could operate equally at both Jupiter and Neptune.
The growing commoditization of the underground economy has given rise to malware delivery networks, which charge fees for quickly delivering malware or unwanted software to a large number of hosts. To provide this service, a key method is the orchest ration of silent delivery campaigns, which involve a group of downloaders that receive remote commands and that deliver their payloads without any user interaction. These campaigns have not been characterized systematically, unlike other aspects of malware delivery networks. Moreover, silent delivery campaigns can evade detection by relying on inconspicuous downloaders on the client side and on disposable domain names on the server side. We describe Beewolf, a system for detecting silent delivery campaigns from Internet-wide records of download events. The key observation behind our system is that the downloaders involved in these campaigns frequently retrieve payloads in lockstep. Beewolf identifies such locksteps in an unsupervised and deterministic manner. By exploiting novel techniques and empirical observations, Beewolf can operate on streaming data. We utilize Beewolf to study silent delivery campaigns at scale, on a data set of 33.3 million download events. This investigation yields novel findings, e.g. malware distributed through compromised software update channels, a substantial overlap between the delivery ecosystems for malware and unwanted software, and several types of business relationships within these ecosystems. Beewolf achieves over 92% true positives and fewer than 5% false positives. Moreover, Beewolf can detect suspicious downloaders a median of 165 days ahead of existing anti-virus products and payload-hosting domains a median of 196 days ahead of existing blacklists.
التعليقات
جاري جلب التعليقات جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
mircosoft-partner

هل ترغب بارسال اشعارات عن اخر التحديثات في شمرا-اكاديميا